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Theoretical and Applied Climatology

, Volume 44, Issue 1, pp 9–24 | Cite as

Diurnal variability of the Earth Radiation Budget: Sampling requirements, time integration aspects and error estimates for the Earth Radiation Budget Experiment (ERBE)

  • M. Rieland
  • E. Raschke
Article

Summary

The diurnal variation of the Earth Radiation Budget and its components require for sparsely temporal sampling a high amount of modeling for the derivation of precise daily averages. In the present study the time integration errors of the regional monthly averages of the Earth Radiation Budget Experiment (Barkstrom, 1984) are estimated for April 1985. For this error assessment we made use of data of the European geostationary satellite Meteosat 2 which narrowbanded measurements have been converted to reasonable estimates of broad-band radiation fluxes. Based on this data set the measurements of the ERBE satellites, ERBS, NOAA 9, and NOAA 10 are simulated. For the time integration the ERBE time integration models are used.

The mean error for the regional monthly average of the net radiation flux varies between — 3 and + 5 W/m2 for the combination of all three satellites. The largest contribution to this uncertainty is given by the time integration of the shortwave fluxes. A new approach for the time integration procedure is presented which is based on the Maximum Entropy spectral analysis of temporal high resolution data sets as provided by geostationary satellites.

This study closes with the estimation of the final error for ERBE regional monthly averages of the net radiation flux, which includes the uncertainties of the instruments, the inversion process and the time integration process. These errors lie between 11.1 W/m2 for single NOAA 9 products and 7.8 W/m2 for the combination of all three satellites. With that the Earth Radiation Budget Experiment fulfills the required accuracy.

Keywords

Time Integration Inversion Process Geostationary Satellite Integration Error Earth Radiation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag 1991

Authors and Affiliations

  • M. Rieland
    • 1
  • E. Raschke
    • 2
  1. 1.Meteorologisches Institut der Universität HamburgHamburg 13Germany
  2. 2.Institut für Physik, GKSS-ForschungszentrumGeesthachtGermany

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